SMG-b1integrin-linescan-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1036.6883</td>\n",
       "      <td>13</td>\n",
       "      <td>sg-Control</td>\n",
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      ],
      "text/plain": [
       "   file_name edge_category  id  b1int_AUC  b1int_peak_height  NLSmSL_cell_1  \\\n",
       "0          1     High_High   1  2877.8604           523.2205       300.9961   \n",
       "1          1     High_High   2  4928.3426           709.3208       499.5153   \n",
       "2          1     High_High   3  1826.4211           480.2500       374.7908   \n",
       "3          1     High_High   4  2481.1303           469.5478       556.1975   \n",
       "4          1     High_High   5  3707.4365           308.6449      1209.5171   \n",
       "\n",
       "   NLSmSL_cell_2  z_position      groups organoid_id     sample_id  \n",
       "0       590.8175          18  sg-Control           1  sg-Control_1  \n",
       "1       524.0477          21  sg-Control           1  sg-Control_1  \n",
       "2       527.0947          21  sg-Control           1  sg-Control_1  \n",
       "3       713.8285          14  sg-Control           1  sg-Control_1  \n",
       "4      1036.6883          13  sg-Control           1  sg-Control_1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in 1st dataset\n",
    "df = pd.read_csv('../data/211103-IF-b1-integrin-LV-188-401-402-Nikon-40xOil/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-188-line-scan-output/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-188-Ecad-quantification-Summary-592.txt')\n",
    "df.head()\n",
    "\n",
    "# 7 organoids of control sgRNA, 5 edges quantified per organoid\n",
    "df['groups'] = ['sg-Control']*35\n",
    "df['organoid_id'] = ['1']*5 + ['2']*5 + ['3']*5 + ['4']*5 + ['5']*5 + ['6']*5 + ['7']*5\n",
    "df['sample_id'] = [df.groups[i] + '_' + df.organoid_id[i] for i in range(len(df))]\n",
    "\n",
    "# pass to new variable\n",
    "df1 = df\n",
    "\n",
    "# Check the header\n",
    "df1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      <td>sg1-Itgb1_1</td>\n",
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       "      <td>1313.9558</td>\n",
       "      <td>17</td>\n",
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       "      <td>541.8799</td>\n",
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       "      <td>4094.5892</td>\n",
       "      <td>4</td>\n",
       "      <td>sg1-Itgb1</td>\n",
       "      <td>1</td>\n",
       "      <td>sg1-Itgb1_1</td>\n",
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      "text/plain": [
       "   file_name edge_category  id  b1int_AUC  b1int_peak_height  NLSmSL_cell_1  \\\n",
       "0          1     High_High   1   452.4394           157.9768       973.7837   \n",
       "1          1     High_High   2   402.8153           104.0432      1310.4095   \n",
       "2          1     High_High   3   933.0017            70.2487      1468.6579   \n",
       "3          1     High_High   4  1316.4910           107.3575      1214.7846   \n",
       "4          1     High_High   5   541.8799           112.6101      2013.6561   \n",
       "\n",
       "   NLSmSL_cell_2  z_position     groups organoid_id    sample_id  \n",
       "0      2836.1450           6  sg1-Itgb1           1  sg1-Itgb1_1  \n",
       "1      1139.9922          10  sg1-Itgb1           1  sg1-Itgb1_1  \n",
       "2      1368.6312          10  sg1-Itgb1           1  sg1-Itgb1_1  \n",
       "3      1313.9558          17  sg1-Itgb1           1  sg1-Itgb1_1  \n",
       "4      4094.5892           4  sg1-Itgb1           1  sg1-Itgb1_1  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in 2nd dataset\n",
    "df = pd.read_csv('../data/211103-IF-b1-integrin-LV-188-401-402-Nikon-40xOil/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-401-line-scan-output/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-401-Ecad-quantification-Summary-6969.txt')\n",
    "df.head()\n",
    "\n",
    "# 7 organoids of control sgRNA, 5 edges quantified per organoid\n",
    "df['groups'] = ['sg1-Itgb1']*35\n",
    "df['organoid_id'] = ['1']*5 + ['2']*5 + ['3']*5 + ['4']*5 + ['5']*5 + ['6']*5 + ['7']*5\n",
    "df['sample_id'] = [df.groups[i] + '_' + df.organoid_id[i] for i in range(len(df))]\n",
    "\n",
    "# pass to new variable\n",
    "df2 = df\n",
    "\n",
    "# Check the header\n",
    "df2.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>855.4341</td>\n",
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       "      <td>19</td>\n",
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       "      <td>sg2-Itgb1_1</td>\n",
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       "      <td>High_High</td>\n",
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       "      <td>144.2008</td>\n",
       "      <td>137.1601</td>\n",
       "      <td>582.1350</td>\n",
       "      <td>1075.5970</td>\n",
       "      <td>17</td>\n",
       "      <td>sg2-Itgb1</td>\n",
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       "      <td>sg2-Itgb1_1</td>\n",
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       "      <td>5</td>\n",
       "      <td>623.1928</td>\n",
       "      <td>76.2010</td>\n",
       "      <td>1144.3643</td>\n",
       "      <td>1136.6274</td>\n",
       "      <td>12</td>\n",
       "      <td>sg2-Itgb1</td>\n",
       "      <td>1</td>\n",
       "      <td>sg2-Itgb1_1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   file_name edge_category  id  b1int_AUC  b1int_peak_height  NLSmSL_cell_1  \\\n",
       "0          1     High_High   1   418.1767            59.6561      1498.8975   \n",
       "1          1     High_High   2    90.1825            28.9904      1718.4635   \n",
       "2          1     High_High   3   715.4984            75.1782       855.4341   \n",
       "3          1     High_High   4   144.2008           137.1601       582.1350   \n",
       "4          1     High_High   5   623.1928            76.2010      1144.3643   \n",
       "\n",
       "   NLSmSL_cell_2  z_position     groups organoid_id    sample_id  \n",
       "0      1914.9096           8  sg2-Itgb1           1  sg2-Itgb1_1  \n",
       "1      1286.8686           8  sg2-Itgb1           1  sg2-Itgb1_1  \n",
       "2       977.7119          19  sg2-Itgb1           1  sg2-Itgb1_1  \n",
       "3      1075.5970          17  sg2-Itgb1           1  sg2-Itgb1_1  \n",
       "4      1136.6274          12  sg2-Itgb1           1  sg2-Itgb1_1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in 3rd dataset\n",
    "df = pd.read_csv('../data/211103-IF-b1-integrin-LV-188-401-402-Nikon-40xOil/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-402-line-scan-output/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-402-Ecad-quantification-Summary-8374.txt')\n",
    "df.head()\n",
    "\n",
    "# 6 organoids of control sgRNA, 5 edges quantified per organoid\n",
    "df['groups'] = ['sg2-Itgb1']*30\n",
    "df['organoid_id'] = ['1']*5 + ['2']*5 + ['3']*5 + ['4']*5 + ['5']*5 + ['6']*5\n",
    "df['sample_id'] = [df.groups[i] + '_' + df.organoid_id[i] for i in range(len(df))]\n",
    "\n",
    "# pass to new variable\n",
    "df3 = df\n",
    "\n",
    "# Check the header\n",
    "df3.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>index</th>\n",
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       "      <th>z_position</th>\n",
       "      <th>groups</th>\n",
       "      <th>organoid_id</th>\n",
       "      <th>sample_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>High_High</td>\n",
       "      <td>1</td>\n",
       "      <td>2877.8604</td>\n",
       "      <td>523.2205</td>\n",
       "      <td>300.9961</td>\n",
       "      <td>590.8175</td>\n",
       "      <td>18</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>High_High</td>\n",
       "      <td>2</td>\n",
       "      <td>4928.3426</td>\n",
       "      <td>709.3208</td>\n",
       "      <td>499.5153</td>\n",
       "      <td>524.0477</td>\n",
       "      <td>21</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>High_High</td>\n",
       "      <td>3</td>\n",
       "      <td>1826.4211</td>\n",
       "      <td>480.2500</td>\n",
       "      <td>374.7908</td>\n",
       "      <td>527.0947</td>\n",
       "      <td>21</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>High_High</td>\n",
       "      <td>4</td>\n",
       "      <td>2481.1303</td>\n",
       "      <td>469.5478</td>\n",
       "      <td>556.1975</td>\n",
       "      <td>713.8285</td>\n",
       "      <td>14</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>High_High</td>\n",
       "      <td>5</td>\n",
       "      <td>3707.4365</td>\n",
       "      <td>308.6449</td>\n",
       "      <td>1209.5171</td>\n",
       "      <td>1036.6883</td>\n",
       "      <td>13</td>\n",
       "      <td>sg-Control</td>\n",
       "      <td>1</td>\n",
       "      <td>sg-Control_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  file_name edge_category  id  b1int_AUC  b1int_peak_height  \\\n",
       "0      0          1     High_High   1  2877.8604           523.2205   \n",
       "1      1          1     High_High   2  4928.3426           709.3208   \n",
       "2      2          1     High_High   3  1826.4211           480.2500   \n",
       "3      3          1     High_High   4  2481.1303           469.5478   \n",
       "4      4          1     High_High   5  3707.4365           308.6449   \n",
       "\n",
       "   NLSmSL_cell_1  NLSmSL_cell_2  z_position      groups organoid_id  \\\n",
       "0       300.9961       590.8175          18  sg-Control           1   \n",
       "1       499.5153       524.0477          21  sg-Control           1   \n",
       "2       374.7908       527.0947          21  sg-Control           1   \n",
       "3       556.1975       713.8285          14  sg-Control           1   \n",
       "4      1209.5171      1036.6883          13  sg-Control           1   \n",
       "\n",
       "      sample_id  \n",
       "0  sg-Control_1  \n",
       "1  sg-Control_1  \n",
       "2  sg-Control_1  \n",
       "3  sg-Control_1  \n",
       "4  sg-Control_1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate the 3 dataframes and reset the index\n",
    "df = pd.concat([df1, df2, df3])\n",
    "df.reset_index(inplace=True)\n",
    "\n",
    "# Check the header\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize all peak height to the average of control\n",
    "ctrl_average = df1.b1int_peak_height.mean()\n",
    "\n",
    "df['b1int_peak_height_normed'] = df['b1int_peak_height'] / ctrl_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "     Multiple Comparison of Means - Tukey HSD, FWER=0.05    \n",
      "===========================================================\n",
      "  group1     group2  meandiff p-adj   lower   upper  reject\n",
      "-----------------------------------------------------------\n",
      "sg-Control sg1-Itgb1  -0.7808  0.001 -0.9408 -0.6207   True\n",
      "sg-Control sg2-Itgb1  -0.8722  0.001 -1.0388 -0.7056   True\n",
      " sg1-Itgb1 sg2-Itgb1  -0.0914 0.3965  -0.258  0.0751  False\n",
      "-----------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.39651125]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f81695906a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df.b1int_peak_height_normed, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='groups', ylabel='b1int_peak_height_normed'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(df.groups, df.b1int_peak_height_normed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots for figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 270x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm plot of 4 categories\n",
    "\n",
    "outputPrefix = '211103-IF-b1-integrin-swarm'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width = 0.9\n",
    "fig_height = 1.2\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "# ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax = fig.add_axes([0.35, 0.4, 0.6, 0.55])\n",
    "\n",
    "ax = sns.swarmplot(x='groups', y='b1int_peak_height_normed', data=df,\n",
    "                   hue='organoid_id',\n",
    "#                    palette={'1': 'red', '2': 'green', '3': 'blue', '4': yellow', '5': 'purple'},\n",
    "#                    color=\"blue\",\n",
    "                   size=1.0, alpha=.8)\n",
    "\n",
    "ax = sns.barplot(x='groups', y='b1int_peak_height_normed', data=df,\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.7, errcolor=\"k\", capsize=.2, ci='sd')\n",
    "\n",
    "plt.ylim(0, 2.5)\n",
    "# plt.yticks([0, 3, 6])\n",
    "\n",
    "plt.xlabel(None)\n",
    "# plt.ylabel(r\"$\\beta$1-integrin intensity\")\n",
    "plt.ylabel('ITGB1 intensity')\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "# rotate x tick labels\n",
    "x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "ax.get_legend().remove()\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x150 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example linescan profile\n",
    "\n",
    "df_example = pd.read_csv('../data/211103-IF-b1-integrin-LV-188-401-402-Nikon-40xOil/211103-IF-E13-Cas9-bud-3d-beta1-integrin-LV-188-line-scan-output/raw-linescan-profile-record/1-2-b1int.txt')\n",
    "\n",
    "df_example['pixels'] = list(range(len(df_example)))\n",
    "\n",
    "outputPrefix = '211103-b1-integrin-line-scan-example'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width=0.5\n",
    "fig_height=0.5\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "plt.plot(df_example.pixels, df_example.b1int_intensity, color='#00dddd')\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sg-Control</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.447805</td>\n",
       "      <td>0.222793</td>\n",
       "      <td>0.709387</td>\n",
       "      <td>0.994499</td>\n",
       "      <td>1.190125</td>\n",
       "      <td>2.251873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sg1-Itgb1</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.219238</td>\n",
       "      <td>0.151135</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>0.114192</td>\n",
       "      <td>0.178813</td>\n",
       "      <td>0.275159</td>\n",
       "      <td>0.725794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sg2-Itgb1</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.127812</td>\n",
       "      <td>0.052035</td>\n",
       "      <td>0.047530</td>\n",
       "      <td>0.098386</td>\n",
       "      <td>0.120830</td>\n",
       "      <td>0.151462</td>\n",
       "      <td>0.312301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      mean       std       min       25%       50%       75%  \\\n",
       "groups                                                                          \n",
       "sg-Control   35.0  1.000000  0.447805  0.222793  0.709387  0.994499  1.190125   \n",
       "sg1-Itgb1    35.0  0.219238  0.151135  0.056301  0.114192  0.178813  0.275159   \n",
       "sg2-Itgb1    30.0  0.127812  0.052035  0.047530  0.098386  0.120830  0.151462   \n",
       "\n",
       "                 max  \n",
       "groups                \n",
       "sg-Control  2.251873  \n",
       "sg1-Itgb1   0.725794  \n",
       "sg2-Itgb1   0.312301  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get descriptive stats for all groups\n",
    "df.groupby('groups').describe()['b1int_peak_height_normed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}